Google Spreadsheets Query
Google Sheets Query
This component uses the Google Sheets API to retrieve data and load it into a table. This stages the data, so the table is reloaded each time. You may then use transformations to enrich and manage the data in permanent tables.
The component offers both a Basic and Advanced mode (see below) for generating the Google Sheets API query.
There are some special pseudo columns which can be part of a query filter, but are not returned as data. This is fully described in the Data Model.
Warning: This component is potentially destructive. If the target table undergoes a change in structure, it will be recreated. Otherwise, the target table is truncated. Setting the Load Option 'Recreate Target Table' to 'Off' will prevent both recreation and truncation. Do not modify the target table structure manually.
|Name||Text||The descriptive name for the component.|
Basic: This mode will build a Google Sheets Query for you using settings from Data Source, Data Selection
and Data Source Filter parameters. In most cases, this will be sufficient.
Advanced: This mode will require you to write an SQL-like query which is translated into one or more Google Sheets API calls. The available fields and their descriptions are documented in the Data Model.
|Authentication||Choice||Select an authentication method, which must be setup in advance. Google Sheets uses the OAuth standard for authenticating 3rd party applications. More help is provided in the setup screens for OAuth authentication.|
|Spreadsheet Name||Text||Enter the name of the sheet. This must be accessible to the account used to authenticate (but that account doesn't need to own the spreadsheet).|
|Contains Header Row||Choice||
Yes - The first row of data (row 1) is used to derive column names. Spaces and special characters are removed.
No - Data is returned using columns A, B, C and so on. All rows are treated as data.
|Cell Range||Text||This is an optional cell range, e.g. A1:C500. If not specified, the entire sheets is read. If specified, then you must set "Contains Header Row" to No, and the range should only include data and not column headings.|
|Data Source||Choice||Select a data source. This will be a list of sheets found, plus the special source "Spreadsheets", which contains metadata about all the spreadsheets accessible. For more information, please see the Data Model.|
|Data Selection||Choice||Select one or more columns to return from the query.|
|Data Source Filter||Input Column||The available input columns vary depending upon the Data Source.|
Is: Compares the column to the value using the comparator.
Not: Reverses the effect of the comparison, so "equals" becomes "not equals", "less than" becomes "greater than or equal to", etc.
|Comparator||Choose a method of comparing the column to the value. Possible comparators include: 'Equal To', 'Greater than', 'Less than', 'Greater than or equal to', 'Less than or equal to', 'Like', 'Null'.
'Equal To' can match exact strings and numeric values while other comparators such as 'Greater than' will work only with numerics. The 'Like' operator allows the wildcard character (%) to be used at the start and end of a string value to match a column. The Null operator matches only Null values, ignoring whatever the value is set to.
Not all data sources support all comparators, thus it is likely only a subset of the above comparators will be available to choose from.
|Value||The value to be compared.|
|SQL Query||Text||This is an SQL-like query, written according to the Google Sheets Data Model.|
|Limit||Number||Fetching a large number of results from Google Sheets will use multiple API calls. These calls are rate-limited by the provider, so fetching a very large number may result in errors.|
|Connection Options||Parameter||A JDBC parameter supported by the Database Driver. The available parameters
are explained in the Data Model.
They are usually not required as sensible defaults are assumed.
|Value||A value for the given Parameter.|
|Storage Account||Select||(Azure Only) Select a Storage Account with your desired Blob Container to be used for staging the data.|
|Blob Container||Select||(Azure Only) Select a Blob Container to be used for staging the data.|
(AWS Only) Snowflake Managed: Allow Matillion ETL to create and use a temporary internal stage on Snowflake for staging the data. This stage, along with the staged data, will cease to exist after loading is complete.
Existing Amazon S3 Location: Selecting this will avail the user of properties to specify a custom staging area on S3.
|S3 Staging Area||Text||(AWS Only) The name of an S3 bucket for temporary storage. Ensure
your access credentials have S3 access and permission to write
to the bucket. See this document for
details on setting up access. The temporary objects created in this bucket will be removed again after the load completes, they are not kept.
This property is available when using an Existing Amazon S3 Location for Staging.
|Type||Select||Choose between using a standard table or an external table.
Standard: The data will be staged on an S3 bucket before being loaded into a table.
External: The data will be put into an S3 Bucket and referenced by an external table.
|Warehouse||Select||Choose a Snowflake warehouse that will run the load.|
|Database||Select||Choose a database to create the new table in.|
|Schema||Select||Select the table schema. The special value, [Environment Default] will use the schema defined in the environment. For more information on using multiple schemas, see this article.|
|Target Table||Text||Provide a new table name.
Warning: This table will be recreated and will drop any existing table of the same name.
|Location||Text/Select||When using an 'External' type table, Provide an S3 Bucket path that will be used to store the data. Once on an S3 bucket, the data can be referenced by the external table.|
|Table Distribution Style||Select||Auto: (Default) Allow Redshift to manage your distribution style.
Even: Distribute rows around the Redshift cluster evenly.
All: Copy rows to all nodes in the Redshift cluster.
Key: Distribute rows around the Redshift cluster according to the value of a key column.
Table distribution is critical to good performance - see the Amazon Redshift documentation for more information.
|Table Distribution Key||Select||This is only displayed if the Table Distribution Style is set to Key. It is the column used to determine which cluster node the row is stored on.|
|Table Sort Key||Select||This is optional, and specifies the columns from the input that should be
set as the table's sort-key.
Sort-keys are critical to good performance - see the Amazon Redshift documentation for more information.
|Sort Key Options||Select||Decide whether the sort key is of a compound or interleaved variety - see the Amazon Redshift documentation for more information.|
|Load Options||Multiple Selection||
Comp Update: Apply automatic compression to the target table (if ON). Default is ON.
Stat Update: Automatically update statistics when filling a table (if ON). Default is ON. In this case, it is updating the statistics of the target table.
Clean S3 Objects: Automatically remove UUID-based objects on the S3 Bucket (if ON). Default is ON. Effectively decides whether to keep the staged data in the S3 Bucket or not.
String Null is Null: Converts any strings equal to "null" into a null value. This is case sensitive and only works with entirely lower-case strings. Default is ON.
Recreate Target Table:Choose whether the component recreates its target table before the data load. If OFF, the existing table will be used. Default is ON.
|Encryption||Select||(AWS Only) Decide on how the files are encrypted inside the S3 Bucket.This property is available when using an Existing Amazon S3 Location for Staging.
None: No encryption.
SSE KMS: Encrypt the data according to a key stored on KMS.
SSE S3: Encrypt the data according to a key stored on an S3 bucket
|KMS Key ID||Select||(AWS Only) The ID of the KMS encryption key you have chosen to use in the 'Encryption' property.|
|Load Options||Multiple Select||Clean Cloud Storage Files: (If On) Destroy staged files on Cloud Storage after loading data. Default is On.
Cloud Storage File Prefix: Give staged file names a prefix of your choice. Default is empty (no prefix).
|Auto Debug||Select||Choose whether to automatically log debug information about your load. These logs can be found in the Task History and should be included in support requests concerning the component. Turning this on will override any debugging Connection Options.|
|Debug Level||Select||The level of verbosity with which your debug information is logged. Levels above 1 can log huge amounts of data and result in slower execution.
1: Will log the query, the number of rows returned by it, the start of execution and the time taken, and any errors.
2: Will log everything included in Level 1, cache queries, and additional information about the request, if applicable.
3: Will additionally log the body of the request and the response.
4: Will additionally log transport-level communication with the data source. This includes SSL negotiation.
5: Will additionally log communication with the data source and additional details that may be helpful in troubleshooting problems. This includes interface commands.
This component makes the following values available to export into variables:
|Time Taken To Stage||The amount of time (in seconds) taken to fetch the data from the data source and upload it to S3.|
|Time Taken To Load||The amount of time (in seconds) taken to execute to COPY statement to load the data into the target table from S3.|
Connect to the target database and issue the query. Stream the results into objects on S3. Then create or truncate the target table and issue a COPY command to load the S3 objects into the table. Finally, clean up the temporary S3 objects.
In this example, we have a Google Sheet filled with release information for Matillion ETL. We want to load this data into a table using Matillion ETL and we will use the Google Sheets Query component to do so. The job layout is shown below.
A Create Table component makes the table we will be using to load data into. Following this, a Google Sheets Query component is connected and configured as shown below. Connecting to the Google Sheets API requires setting up OAuth credentials as described here. In the 'Data Selection' property, we bring in all columns and set the Limit to 10000 rows which should be more than enough for our transformation job.
With the components set up, we can run this job by right-clicking the canvas and selecting 'Run Job'. The job will then be tracked in the 'Tasks' tab and will show a green check mark when completed.
This data can then be sampled in a Transformation job using the Table Input component to check the load has worked as intended.